Implementation Details.    Our model is implemented in PyTorch [1]}, and trained end-to-end with a mini-batch size of 8 per NVIDIA RTX A6000 GPU. During training, we use the AdamW optimizer and clip gradients to the range \([-1, 1]\) . When generating the cost volume by plane-sweep stereo, we set the plane hypotheses number as \(M_0\) =64; while predicting the final depth using index field, we set the plane hypotheses number as \(M_1\) =256. The same hyperparameters as in [2]} are adopted for the context network and 3-level GRU architecture.
